UPDATE (2014.03.06): there is an option in the NVIDIA control panel to enable or disable full speed FP64 support for the GeForce GTX Titan. By default this option is set to OFF. But full speed FP64 support comes with a price: FP32 performance is impacted as it’s described in the NVCPL:

Note that turning on this setting reduces performance for all non-CUDA programs, including games

When FP64 is enabled, the Julia FP64 score is this time correct and is around 1/3 FP32 (FP32 when FP64 is OFF). But when the FP64 is ON, FP32 performance drops by around 15%.

In the freshly released GpuTest 0.7.0 (for Windows, Linux and Mac OS X), we can find a Julia fractal rendered in GLSL using double precision floating point (or FP64) numbers. The Julia FP64 test requires an OpenGL 4 capable GPU and the support of the GL_ARB_gpu_shader_fp64 extension.

The Julia fractal is also available with FP32 (single precision floating point) numbers. That allows some comparisons like the famous ratio between FP32 and FP64 we can read in many reviews(fp64 = 1/xx fp32)…

With current graphics drivers (Catalyst 14.2 beta for AMD Radeon and R319/R334 for NVIDIA), AMD Radeon GPUs are faster than NVIDIA GPUs: in FP32, the Radeon HD 7970 is +27% faster than the GeForce GTX Titan. And the gap is much more important in FP64 where the HD 7970 is 6X faster than the GTX Titan.

If we look at this GFLOPS comparative table, the ratio between FP32 and FP64 is more or less ok for the Radeon HD 7970 (around 1/3 FP32 while the official/marketing ratio is FP64 = 1/4 FP32) but is meaningless for the GTX Titan: the official ratio is FP64 = 1/3 FP32 while in this test, the ratio is rather FP64 = 1/11 FP32. Same thing for the GTX 750 Ti (Maxwell) and for the GTX 680.

In full HD 1920×1080 resolution, the ratio stays the same (FP64 = 1/11 FP32 for the GTX Titan, see HERE and HERE).

Possible causes of this sluggishness in FP64:

the GeForce driver (the OpenGL part) is not optimized for this particular use case.

there is something wrong in the GLSL code of the Julia fractal that slows down GeForce GPUs. The Julia fractal used in GpuTest is based on this article.

GTX Titan FP64 performance is not 1/3 FP32

something limits the fp64 performance inside the GeForce driver…

other???

MAIN REASON: full speed FP64 is disabled by default on GTX Titan. FP64 can be enabled in NVIDIA control panel.

Strange… Let me hazard a guess : did you enable the double precision option in the control panel ? It has to be explicitly enabled to allow full speed FP64 on Titan. By default Titan is restricted to 1/24 performance, like the GTX 680

lol you should’ve known that Titan has two different modes switchable through driver, I’m surprised you didn’t, there are a lot of articles about it 😛 Game mode is for people who don’t need GPGPU and use Titan only for gaming.

@nuninho1980:
This app is a benchmark. So it is supposed to test the actual performance of the GPUs running the same code. Would you look at a benchmark in which the products are compared with different tests protocols? Certainly not. So I think that JeGX should not add cuda support…
If opencl drivers are bad with geforce gpus, then nvidia should work on the drivers. But JeGX is not the one who should work to make their product shine…

I’m not paid by AMD nor by NVIDIA (that would be cool but it’s not the case). I’m totally independent. I don’t understand why we’re talking about OpenCL / CUDA here. I just did a simple OpenGL 4 (and from what I know, both AMD and NVIDIA support OpenGL) test with the FP64 feature exposed by this universal API. With current drivers, HD 7970 is faster than GTX Titan (in this particular test) and that’s all.
Maybe in few weeks, NVIDIA’s GPU will be faster than AMD one. Now regarding CUDA or OpenCL. CUDA is certainly an extremely powerful GPU computing
solution but today I try to code cross-platform apps that work on NVIDIA, AMD and Intel GPUs. So if I had to code now a GPU computing benchmark, I would do it using OpenCL. And I’m sure (I hope!) that one day NVIDIA will release a super OpenCL driver. But I never said that I won’t code a CUDA benchmark. It would be even interesting to compare the same algorithm in CUDA and in OpenCL on different hardware (hummm, my brain is already thinking to a possible bench, aargghh!). Coding, releasing and updating a soft takes time (and more when it’s cross-platform) so be patient, and maybe one day you will read on Geeks3D a new about a new cuda benchmark… Good night!

@John Smith
I’am a geek, I don’t care about marketing stuff :). CUDA is a great piece of technology (I use it everyday), but here it’s OpenCL only (for now). It’s only that, not any conspiracy involved…

@blacksheep: Ok but GTX Titan (CUDA) would ALWAYS beat any AMD-ATi (OCL), if OpenGL will use CUDA because GeForce card continues get abnormal performance since the “poor” optimization of OpenCL 1.1 driver for GeForce.

@up – read more carefully
“we can find a Julia fractal rendered in GLSL using double precision floating point (or FP64) numbers”
GLSL != OpenCL
GLSL = OpenGL
If it was CL kernel it surely wouldn’t be “GLSL” and also would not require that GL_ARB_gpu_shader_fp64 particular extension.
gputest alone probably uses OpenCL somewhere, but not in this test.
So I guess NV cut FP64 performance in that particular area very much too since I am able to get some 3700pts at default settings with 7750 @FP64 which is way more than GTX750

It’s a pure OpenGL test (GLSL shaders with fp64 variables), there’s no OpenCL kernel involved in the rendering. The OpenCL plugin is currently used only with the GPU monitoring plugin to provide information for the GPU database.

Got R9 280x yesterday and have just tested it with Cat 14.6 beta. My videoboard is XFX Black with overclocked chip 1080 instead of 1000 for 7970. I got 128273 points, 2135fps for FP32 and 47934 points, 797fps for FP64